Covid & Economics Case for Rich & Poor Countries. Advanced Visualizations in R.

1. Libraries

Unnecessary staff

2. Data Load

3. Data Description

glimpse(data)

Rows: 61,486
Columns: 55
$ iso_code                              <chr> "AFG", "AFG", "AFG", "AFG", "...
$ continent                             <chr> "Asia", "Asia", "Asia", "Asia...
$ location                              <chr> "Afghanistan", "Afghanistan",...
$ date                                  <date> 2020-02-24, 2020-02-25, 2020...
$ total_cases                           <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 4,...
$ new_cases                             <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 2,...
$ new_cases_smoothed                    <dbl> NA, NA, NA, NA, NA, 0.143, 0....
$ total_deaths                          <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_deaths                            <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_deaths_smoothed                   <dbl> NA, NA, NA, NA, NA, 0, 0, 0, ...
$ total_cases_per_million               <dbl> 0.026, 0.026, 0.026, 0.026, 0...
$ new_cases_per_million                 <dbl> 0.026, 0.000, 0.000, 0.000, 0...
$ new_cases_smoothed_per_million        <dbl> NA, NA, NA, NA, NA, 0.004, 0....
$ total_deaths_per_million              <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_deaths_per_million                <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_deaths_smoothed_per_million       <dbl> NA, NA, NA, NA, NA, 0, 0, 0, ...
$ reproduction_rate                     <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ icu_patients                          <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ icu_patients_per_million              <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ hosp_patients                         <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ hosp_patients_per_million             <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ weekly_icu_admissions                 <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ weekly_icu_admissions_per_million     <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ weekly_hosp_admissions                <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ weekly_hosp_admissions_per_million    <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ total_tests                           <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_tests                             <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ total_tests_per_thousand              <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_tests_per_thousand                <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_tests_smoothed                    <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_tests_smoothed_per_thousand       <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ positive_rate                         <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ tests_per_case                        <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ tests_units                           <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ total_vaccinations                    <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_vaccinations                      <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_vaccinations_smoothed             <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ total_vaccinations_per_hundred        <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ new_vaccinations_smoothed_per_million <lgl> NA, NA, NA, NA, NA, NA, NA, N...
$ stringency_index                      <dbl> 8.33, 8.33, 8.33, 8.33, 8.33,...
$ population                            <dbl> 38928341, 38928341, 38928341,...
$ population_density                    <dbl> 54.422, 54.422, 54.422, 54.42...
$ median_age                            <dbl> 18.6, 18.6, 18.6, 18.6, 18.6,...
$ aged_65_older                         <dbl> 2.581, 2.581, 2.581, 2.581, 2...
$ aged_70_older                         <dbl> 1.337, 1.337, 1.337, 1.337, 1...
$ gdp_per_capita                        <dbl> 1803.987, 1803.987, 1803.987,...
$ extreme_poverty                       <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ cardiovasc_death_rate                 <dbl> 597.029, 597.029, 597.029, 59...
$ diabetes_prevalence                   <dbl> 9.59, 9.59, 9.59, 9.59, 9.59,...
$ female_smokers                        <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ male_smokers                          <dbl> NA, NA, NA, NA, NA, NA, NA, N...
$ handwashing_facilities                <dbl> 37.746, 37.746, 37.746, 37.74...
$ hospital_beds_per_thousand            <dbl> 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,...
$ life_expectancy                       <dbl> 64.83, 64.83, 64.83, 64.83, 6...
$ human_development_index               <dbl> 0.498, 0.498, 0.498, 0.498, 0...

# data <- read.csv('owid-covid-data.csv', stringsAsFactors = F)
data$date <- as.Date(data$date, "%Y-%m-%d")
knitr::kable(data[1:5, ])

4. Aggregating Data

library(dplyr)
temp <- data.frame(data) %>% dplyr::select(location, continent, gdp_per_capita, population_density, 
    total_cases, total_cases_per_million, population)

temp[is.na(temp)] <- 0

temp <- subset(temp, location != "World" & location != "International")
data_agg <- temp %>% group_by(location, continent) %>% summarise(total_cases = max(total_cases), 
    gdp_per_capita = max(gdp_per_capita), population_density = max(population_density), 
    total_cases_per_million = max(total_cases_per_million), population = max(population))


knitr::kable(data_agg[1:10, ], capture = "Dataset first 10 records")

5. Worldmap

5. Distributions

library(ggplot2)
library(scales)

ggplot(data_agg, aes(x = gdp_per_capita)) + geom_histogram(aes(y = ..density..), 
    colour = "black", fill = "white") + geom_density(color = "darkblue", fill = "#E69F00", 
    alpha = 0.7) + labs(title = "What is the Distribution of GDP per capita?", subtitle = "Right-skewed distribution, many poor countries") + 
    theme(plot.title = element_text(size = 16, color = "#0B8389", face = "bold"), 
        plot.subtitle = element_text(size = 11)) + scale_y_continuous(labels = comma) + 
    scale_x_continuous(labels = comma)

library(DT)
data_agg$gdp_per_capita_group <- cut(data_agg$gdp_per_capita, breaks = c(0, 7000, 
    20000, 40000), include.lowest = FALSE, dig.lab = 6)


data_for_dist.a <- data_agg %>% group_by(continent) %>% dplyr::count(gdp_per_capita_group)
data_for_dist.a <- na.omit(data_for_dist.a)

# datatable(head(data_for_dist.a), caption = 'Distribution of countries by
# GDP_per_capita groups & continents')

wrapper <- function(x, ...) {
    paste(strwrap(x, ...), collapse = "\n")
}

ggplot(data_for_dist.a, aes(x = continent, y = gdp_per_capita_group)) + geom_tile(aes(fill = n), 
    color = "white", show.legend = F) + theme_minimal() + geom_text(aes(label = n), 
    size = 5, fontface = "bold", color = "white") + scale_fill_gradient(low = "#6bb0c4", 
    high = "#dd5f64") + theme(panel.grid = element_blank(), plot.title = element_text(size = 16, 
    color = "#0B8389", face = "bold"), plot.subtitle = element_text(size = 11), axis.text.y = element_text(size = 10), 
    axis.text.x = element_text(size = 10)) + ggtitle(wrapper("Distribution of countries by GDP_per_capita groups & continents", 
    width = 60)) + labs(subtitle = wrapper("Moldova in Europe and Bolivia in South America only countries with less than $7,000 GDP_per_capita", 
    width = 80))

library(plotly)
library(ggrepel)

data_agg$population_mln <- as.numeric(data_agg$population/1e+06)
ggplot(data_agg, aes(x = total_cases, y = gdp_per_capita, color = continent, size = population_mln)) + 
    geom_point(alpha = 0.5) + labs(x = "Total Cases", y = "GDP per capita", title = "Where are European countries on the scatterplot?", 
    subtitle = "Just on the top-right, behind the US!", caption = "Date: 2021-Jan-29") + 
    theme(panel.background = element_rect(fill = "white", color = "white", size = 1.2), 
        plot.background = element_rect(fill = "white"), plot.title = element_text(size = 16, 
            face = "bold", color = "#0B8389"), plot.subtitle = element_text(size = 11), 
        plot.caption = element_text(face = "italic", size = 8), panel.grid.major = element_line(linetype = "dashed", 
            size = 0.1), panel.grid.minor = element_blank(), axis.text.y = element_text(size = 10), 
        axis.text.x = element_text(size = 10), legend.position = "bottom") + scale_x_continuous(trans = "log10", 
    labels = comma) + scale_y_continuous(trans = "log10", labels = comma) + scale_size(range = c(0.1, 
    20), name = "Population [mln]") + guides(color = guide_legend(override.aes = list(size = 6)))

# ggplotly(a)

7. Heatmap to show I and II wave

library(lubridate)
library(dplyr)

EU <- c("Austria", "Belgium", "Bulgaria", "Croatia", "Cyprus", "Czech Republic", 
    "Denmark", "Estonia", "Finland", "France", "Germany", "Greece", "Hungary", "Ireland", 
    "Italy", "Latvia", "Lithuania", "Luxembourg", "Malta", "Netherlands", "Poland", 
    "Portugal", "Romania", "Slovakia", "Slovenia", "Spain", "Sweden")


data_for_month <- data.frame(data) %>% dplyr::select(location, date, new_cases) %>% 
    filter(location %in% EU)
data <- data.frame(data)

datats <- data.frame(data) %>% dplyr::select(continent, date, total_cases)


data_for_month$month <- floor_date(data_for_month$date, "month")

data_for_month <- as.tibble(data_for_month) %>% group_by(location, month) %>% summarize(new_cases = as.numeric(sum(new_cases)))

data_for_month$month <- as.character(data_for_month$month)
data_for_month$new_cases_1000 <- as.numeric(data_for_month$new_cases/1000)
ggplot(data_for_month, aes(month, location)) + geom_tile(aes(fill = new_cases_1000), 
    colour = "white") + labs(title = "Which month had the biggest amount of new cases?", 
    subtitle = "First wave in Mar 2020 and the second wave in Sep-Oct 2020!", caption = "Date: 2021-Jan-29") + 
    scale_x_discrete("", expand = c(0, 0)) + scale_y_discrete("", expand = c(0, 0)) + 
    scale_fill_gradient2(name = "New Cases 000'", low = "#006400", mid = "#f2f6c3", 
        high = "#cd0000", midpoint = 0.5, na.value = "white") + theme(legend.position = "right", 
    axis.ticks = element_blank(), axis.text.x = element_text(angle = 90, hjust = 0.5), 
    axis.text.y = element_text(size = 10), panel.background = element_blank(), plot.title = element_text(size = 16, 
        face = "bold", color = "#0B8389"), plot.subtitle = element_text(size = 11))

new_data_agg <- data_agg[order(-data_agg$total_cases), ]
new_data_agg$total_cases_mln <- as.numeric(new_data_agg$total_cases/1e+06)
new_data_agg <- new_data_agg[1:15, ]
ggplot(data = new_data_agg, aes(x = total_cases_mln, y = reorder(location, total_cases_mln), 
    fill = continent)) + geom_bar(width = 0.8, stat = "identity") + labs(title = "Which are top 10 countries by Total Cases?", 
    subtitle = "USA, India & Brazil combines 45% of World's Total Cases", caption = "Date: 2021-Jan-29") + 
    xlab("Total Cases [mln]") + ylab("Countries") + theme(legend.position = "right", 
    text = element_text(size = 12), axis.text.y = element_text(hjust = 1), plot.caption = element_text(face = "italic", 
        size = 8), plot.title = element_text(size = 16, face = "bold", color = "#0B8389"), 
    plot.subtitle = element_text(size = 11)) + scale_fill_discrete(name = "Continent") + 
    geom_text(aes(label = round(total_cases_mln, 3)), vjust = 0.5, hjust = -0.03, 
        color = "darkgreen", size = 3.5) + scale_fill_brewer(palette = "Paired")

library(ggiraph)
library(ggplot2)
library(ggrepel)
# Basic plot:
data_agg_n <- data_agg %>% filter(gdp_per_capita > 10 & total_cases_per_million > 
    10)

g <- ggplot(data_agg_n, aes(x = gdp_per_capita, y = total_cases_per_million, color = continent)) + 
    geom_text_repel(data = subset(data_agg_n, gdp_per_capita > 72000), aes(label = location), 
        size = 5, box.padding = unit(0.35, "lines"), point.padding = unit(0.3, "lines"), 
        nudge_x = 0.8, nudge_y = 1.5, direction = "y", hjust = "left", segment.curvature = -0.1, 
        segment.ncp = 3, segment.angle = 20) + geom_point() + labs(title = "How is covid situation in rich countries?", 
    subtitle = "Europe cluster on the right-top while Africa cluster on the left bottom", 
    caption = "Date: 2021-Jan-29") + scale_x_continuous(trans = "log10", expand = expansion(mult = 0.5), 
    labels = comma) + scale_y_continuous(trans = "log10", labels = comma) + theme(legend.position = "bottom", 
    text = element_text(size = 12), plot.caption = element_text(face = "italic", 
        size = 8), plot.title = element_text(size = 16, face = "bold", color = "#0B8389"), 
    plot.subtitle = element_text(size = 11)) + coord_cartesian(clip = "off") + guides(color = guide_legend(override.aes = list(size = 6)))

# Interactiveness:
g_int <- g + geom_point_interactive(aes(tooltip = location), size = 3)

# Wyswietlenie
ggiraph(code = print(g_int))

# geom_label_repel(aes(gdp_per_capita, total_cases_per_million, fill=continent,
# label = location))

ggplot(data_agg, aes(x = gdp_per_capita, fill = continent)) + geom_histogram(data = data_agg[, 
    -5], alpha = 0.5, bins = 30, colour = "black") + labs(title = "Which continents have similar distribution of GDP_per_capita?", 
    subtitle = "Africa and Asia very much right-skewed", caption = "Date: 2021-Jan-29") + 
    facet_wrap(~continent) + guides(fill = FALSE) + theme(axis.text.x = element_text(colour = "grey20", 
    size = 10, angle = 90, hjust = 0.5, vjust = 0.5), axis.text.y = element_text(colour = "grey20", 
    size = 10), text = element_text(size = 12), plot.caption = element_text(face = "italic", 
    size = 8), plot.title = element_text(size = 16, face = "bold", color = "#0B8389"), 
    plot.subtitle = element_text(size = 11)) + scale_y_continuous(labels = comma) + 
    scale_x_continuous(labels = comma)

# ggplotly(b)

data_sel <- data.frame(data) %>% dplyr::select(continent, location, date, new_cases, 
    new_deaths, total_cases, total_deaths, total_cases_per_million, population)

knitr::kable(data_sel[1:5, ])

# datatable(head(data_sel), caption = 'Table 1: This is a simple caption for the
# table.')

# data_anim <- data_sel %>% filter(location %in% c('Germany','Canada', 'Mexico',
# 'Italy', 'Spain', 'Poland'))

data_anim <- data_sel %>% filter(continent %in% c("Africa", "Asia", "Europe", "North America", 
    "South America", "Oceania"))
data_anim <- data_anim[complete.cases(data_anim), ]
gifplot <- ggplot(data_anim, aes(total_cases, total_deaths, size = population, colour = continent)) + 
    geom_point(alpha = 0.7, show.legend = FALSE) + scale_size(range = c(2, 12)) + 
    scale_x_log10() + facet_wrap(~continent) + # Here comes the gganimate specific bits
labs(title = "How virus spread in each continent in 2020?", subtitle = "Year: {frame_time}", 
    x = "Total Cases", y = "Total Deaths") + transition_time(date) + ease_aes("linear") + 
    shadow_wake(wake_length = 0.1, alpha = FALSE) + shadow_mark(alpha = 0.3, size = 0.5) + 
    theme(text = element_text(size = 12), plot.title = element_text(size = 16, face = "bold", 
        color = "#0B8389"), plot.subtitle = element_text(size = 14))


gganimate::animate(gifplot, duration = 15, fps = 20, renderer = gifski_renderer())
anim_save("gifplot.gif")

library(tidyverse)
library(rnaturalearth)
library(cowplot)
library(sf)
library(ggmap)
library(leaflet)
library("rnaturalearth")
library("rnaturalearthdata")
library("sf")
library("rgeos")
library(tidyverse)
library(ggplot2)
library(readr)
library(maps)
library(viridis)
library(plotly)

theme_set(theme_bw())

world <- ne_countries(scale = "medium", returnclass = "sf")
world <- data.frame(world)
world <- world %>% dplyr::rename(location = name)

worldmap <- world %>% dplyr::select(location, adm0_a3)
worldmap1 <- merge(x = worldmap, y = data_agg, by = "location", all.x = TRUE)
fig <- plot_ly(worldmap1, type = "choropleth", locations = worldmap1$adm0_a3, z = worldmap1$gdp_per_capita, 
    colors = "PuBuGn", hoverinfo = "text", text = ~paste("</br> Country: ", worldmap1$location, 
        "</br> GDP_per_capita: ", round(worldmap1$gdp_per_capita, 0), "</br> Total Cases: ", 
        worldmap1$total_cases, "</br> Cases per mln: ", round(worldmap1$total_cases_per_million, 
            3), "</br> Population mln: ", round(worldmap1$population_mln, 3))) %>% 
    layout(title = "All information about Countries, coloured according to GDP_per_capita", 
        size = 16, face = "bold", color = "#0B8389", legend = list(title = list(text = "<b> GDP_per_capita </b>"), 
            orientation = "h"))

fig

# library(htmlwidgets)

# saveWidget(fig, 'index.html')

library(ggplot2)
library(ggcorrplot)
library(tidyverse)
library(lubridate)
library(ggplot2)
library(readxl)
library(gganimate)
library(ggthemes)
library(MASS)
library(reshape2)
library(reshape)
library(DescTools)
library(dplyr)
library(stats)
options(scipen = 999)


exchange_rate_euro <- read.csv("eurofxref-hist.csv", stringsAsFactors = FALSE)
exchange_rate_euro <- exchange_rate_euro[, c(1:42)]
exchange_rate_euro[, c(2:42)] <- lapply(exchange_rate_euro[, c(2:42)], as.numeric)
exchange_rate_euro[is.na(exchange_rate_euro)] = 0

exchange_rate_euro[, 1] <- as.Date(exchange_rate_euro[, 1], "%Y-%m-%d")
exchange_rate_euro <- exchange_rate_euro %>% filter(Date >= as.Date("2020-01-01"))
df2 <- melt(exchange_rate_euro, id = c("Date"))
colnames(df2)[2:3] <- c("Currency", "Exchange_rate")
df <- df2[df2$Currency %in% c("USD"), ]
head(df)

        Date Currency Exchange_rate
1 2021-01-21      USD        1.2158
2 2021-01-20      USD        1.2101
3 2021-01-19      USD        1.2132
4 2021-01-18      USD        1.2064
5 2021-01-15      USD        1.2123
6 2021-01-14      USD        1.2124

# plot<- df2 %>% ggplot( aes(x=Date, y=Exchange_rate,color = Currency)) +
# geom_line() plot


currency_vs_country <- read.csv("tableconvert_csv_9gbdx9.csv")
colnames(currency_vs_country)[1] <- "Country"
temp1 <- merge(x = data_sel, y = currency_vs_country, by.x = "location", by.y = "Country", 
    all.x = TRUE)
temp2 <- merge(x = temp1, y = df2, by.x = c("Code", "date"), by.y = c("Currency", 
    "Date"), all.x = TRUE)
temp2 <- temp2[complete.cases(temp2), ]
temp2 <- temp2[temp2$Exchange_rate < 100, ]


temp3 <- temp2 %>% group_by(date, continent) %>% dplyr::summarize(Mean_ex = mean(Exchange_rate, 
    na.rm = TRUE), cases_pm = sum(new_cases)/max(population))
# temp4 <- melt(temp3, measure.vars =c('Mean_ex', 'Mean_ex'))
data_sub2 <- temp3 %>% filter(continent %in% c("Africa", "Asia", "Europe", "North America", 
    "South America", "Oceania"))
# data_sub2 <- temp3 %>% filter(continent %in% c( 'Europe'))

# data_sub2 <- data_sub2[data_sub2$Mean < 100,]

head(data_sub2)

# A tibble: 6 x 4
# Groups:   date [6]
  date       continent Mean_ex     cases_pm
  <date>     <chr>       <dbl>        <dbl>
1 2020-01-23 Asia         7.69 0.0000000660
2 2020-01-24 Asia         7.65 0.000000192 
3 2020-01-27 Asia         7.65 0.000000557 
4 2020-01-28 Asia         7.63 0.00000183  
5 2020-01-29 Asia         7.63 0.000000402 
6 2020-01-30 Asia         7.65 0.00000143  

library(hrbrthemes)
library(viridis)
library(ggplot2)
library(gganimate)
library(babynames)
library(ggrepel)

plot1 <- data_sub2 %>% ggplot(aes(x = date, y = Mean_ex, group = continent, color = continent)) + 
    geom_line() + geom_point() + geom_label_repel(aes(label = continent), nudge_x = 1, 
    na.rm = TRUE) + scale_color_viridis(discrete = TRUE) + labs(title = "Exchange Rate Trend by Continent", 
    x = "Date", y = "Exchange Rate") + theme(legend.position = "none", text = element_text(size = 12), 
    plot.title = element_text(size = 16, face = "bold", color = "#0B8389")) + transition_reveal(date)
plot1

anim_save("plot1.gif")

library(ggrepel)

plot2 <- data_sub2 %>% ggplot(aes(x = date, y = cases_pm, group = continent, color = continent), 
    show.Legend = FALSE) + geom_line() + geom_point() + geom_label_repel(aes(label = continent), 
    nudge_x = 1, na.rm = TRUE) + scale_color_viridis(discrete = TRUE) + labs(title = "Covid Cases per Capita Trend", 
    x = "Date", y = "Covid Cases per Capita") + theme(legend.position = "none", text = element_text(size = 12), 
    plot.title = element_text(size = 16, face = "bold", color = "#0B8389")) + transition_reveal(date)


gganimate::animate(plot2, duration = 20, fps = 20, renderer = gifski_renderer())

anim_save("plot2.gif")

library(treemap)
library(htmlwidgets)
# devtools::install_github('timelyportfolio/d3treeR')
library(d3treeR)

tree_df <- temp2 %>% group_by(continent, location) %>% mutate(Norm = Exchange_rate/max(Exchange_rate)) %>% 
    dplyr::select(continent, location, Norm)
tree_df <- data.frame(tree_df)

World <- treemap(tree_df, index = c("continent", "location", "Norm"), vSize = "Norm", 
    type = "index", palette = "Set3", bg.labels = c("white"), align.labels = list(c("center", 
        "center"), c("right", "bottom")))

# make it interactive ('rootname' becomes the title of the plot):
inter <- d3tree3(World)

# save the widget library(htmlwidgets)
saveWidget(inter, file = paste0(getwd(), "/interactiveTreemap.html"))

# library(broom.mixed)
library(jtools)
library(arm)
library(dplyr)


dat <- data[, c("continent", "date", "location", "total_cases_per_million", 
    "population", "gdp_per_capita", "stringency_index", "median_age", 
    "human_development_index")]

join1 <- merge(x = dat, y = currency_vs_country, by.x = "location", 
    by.y = "Country", all.x = TRUE)
join2 <- merge(x = join1, y = df2, by.x = c("Code", "date"), 
    by.y = c("Currency", "Date"), all.x = TRUE)

join2 <- join2[complete.cases(join2), ]
head(join2)

     Code       date location continent total_cases_per_million population gdp_per_capita stringency_index median_age human_development_index CountryCode Currency Exchange_rate
3323  BGN 2020-03-09 Bulgaria    Europe                   0.576    6948445       18563.31            21.30       44.7                   0.813          BG      Lev        1.9558
3324  BGN 2020-03-10 Bulgaria    Europe                   0.576    6948445       18563.31            21.30       44.7                   0.813          BG      Lev        1.9558
3325  BGN 2020-03-11 Bulgaria    Europe                   1.007    6948445       18563.31            26.85       44.7                   0.813          BG      Lev        1.9558
3326  BGN 2020-03-12 Bulgaria    Europe                   1.007    6948445       18563.31            26.85       44.7                   0.813          BG      Lev        1.9558
3327  BGN 2020-03-13 Bulgaria    Europe                   3.310    6948445       18563.31            50.93       44.7                   0.813          BG      Lev        1.9558
 [ reached 'max' / getOption("max.print") -- omitted 1 rows ]

join2 <- join2 %>% group_by(continent, location) %>% mutate(Norm = Exchange_rate/max(Exchange_rate))
reg_df <- join2 %>% group_by(continent, location) %>% mutate(Norm_cases = total_cases_per_million/max(total_cases_per_million))
reg_df <- reg_df[, !(colnames(reg_df) %in% c("CountryCode", "Code", 
    "location", "total_cases_per_million", "date"))]
reg_df <- na.omit(reg_df)
# reg_df$date<-as.character(reg_df$date)

# reg_df <- reg_df [,c('date','location', 'continent', 'Norm','Norm_cases')]
head(reg_df, 30)

# A tibble: 30 x 10
# Groups:   continent [1]
   continent population gdp_per_capita stringency_index median_age human_development_index Currency Exchange_rate  Norm Norm_cases
   <chr>          <dbl>          <dbl>            <dbl>      <dbl>                   <dbl> <chr>            <dbl> <dbl>      <dbl>
 1 Europe       6948445         18563.             21.3       44.7                   0.813 Lev               1.96     1  0.0000189
 2 Europe       6948445         18563.             21.3       44.7                   0.813 Lev               1.96     1  0.0000189
 3 Europe       6948445         18563.             26.8       44.7                   0.813 Lev               1.96     1  0.0000331
 4 Europe       6948445         18563.             26.8       44.7                   0.813 Lev               1.96     1  0.0000331
 5 Europe       6948445         18563.             50.9       44.7                   0.813 Lev               1.96     1  0.000109 
 6 Europe       6948445         18563.             50.9       44.7                   0.813 Lev               1.96     1  0.000246 
 7 Europe       6948445         18563.             56.5       44.7                   0.813 Lev               1.96     1  0.000317 
 8 Europe       6948445         18563.             70.4       44.7                   0.813 Lev               1.96     1  0.000435 
 9 Europe       6948445         18563.             70.4       44.7                   0.813 Lev               1.96     1  0.000444 
10 Europe       6948445         18563.             70.4       44.7                   0.813 Lev               1.96     1  0.000600 
# ... with 20 more rows

library(moonBook)
library(jtools)



Reg2 <- lm(formula = Norm ~ Norm_cases + continent, data = reg_df, 
    intercept = FALSE)
# Reg2$pred
summ(Reg2, exp = TRUE)

MODEL INFO:
Observations: 6360
Dependent Variable: Norm
Type: OLS linear regression 

MODEL FIT:
F(6,6353) = 176.72, p = 0.00
R² = 0.14
Adj. R² = 0.14 

Standard errors: OLS
-----------------------------------------------------------
                                Est.   S.E.   t val.      p
---------------------------- ------- ------ -------- ------
(Intercept)                     0.91   0.00   313.03   0.00
Norm_cases                      0.03   0.00    19.12   0.00
continentAsia                   0.02   0.00     5.78   0.00
continentEurope                 0.04   0.00    14.15   0.00
continentNorth America          0.02   0.00     6.35   0.00
continentOceania               -0.00   0.00    -1.01   0.31
continentSouth America         -0.02   0.00    -5.02   0.00
-----------------------------------------------------------

check <- data.frame(summary(Reg2)$coef[summary(Reg2)$coef[, 4] <= 
    0.001, 4])
check <- cbind(Variables = rownames(check), check)
rownames(check) <- 1:nrow(check)
colnames(check)[2] <- "P_Value"
check <- na.omit(check)
check <- check[order(check$P_Value), ]
# heck$P_Value <- round(check$P_Value,digits = 3)




p <- ggplot(Reg2, aes_string(x = names(Reg2$model)[2], y = names(Reg2$model)[3], 
    color = names(Reg2$model)[1])) + geom_col() + scale_fill_distiller(palette = "Reds", 
    direction = 1) + guides(col = guide_legend("Exchange Rate")) + 
    ggtitle("How does Exchange Rate differ in a Continent with given no. of Covid Cases?") + 
    # geom_text(aes(label=formatC(P_Value, format = 'f', digits =7))) +
theme_minimal() + xlab("Covid Cases") + ylab("Continent") + coord_flip() + 
    theme(panel.grid = element_blank(), panel.grid.major.y = element_line(color = "white"), 
        panel.ontop = TRUE)

p + transition_states(continent, wrap = FALSE) + shadow_mark() + 
    enter_grow() + enter_fade()

anim_save("plot3.gif")

library(sandwich)
# plot_summs(Reg)
ggplotly(effect_plot(Reg2, pred = Norm_cases, interval = TRUE, 
    colors = "purple", x.label = "Covid Cases", y.label = "Exchange rate", 
    robust = "HC0", main.title = "Actuals versus Predicted"))

ggplotRegression <- function(Reg) {
    
    require(ggplot2)
    require(ggthemes)
    
    ggplot(Reg$model, aes_string(x = names(Reg$model)[2], y = names(Reg$model)[1], 
        color = names(Reg$model)[3])) + geom_point() + ggtitle("Fit of the model") + 
        theme(plot.title = element_text(hjust = 0.5)) + xlab("Covid Cases") + 
        ylab("Exchange Rate") + guides(col = guide_legend("Continents")) + 
        stat_smooth(method = "lm", col = "red") + labs(subtitle = paste("Adj R2 = ", 
        signif(summary(Reg)$adj.r.squared, 5), "Intercept =", 
        signif(Reg$coef[[1]], 5), " Slope =", signif(Reg$coef[[2]], 
            5), " P =", round(summary(Reg)$coef[2, 4], 2)))
}
ggplotRegression(Reg2)